Integrated analysis of aboveground and belowground indicators support a comprehensive evaluation of ecosystem recovery
Bibliographic record
Abstract
Analyses of diverse aboveground and belowground indicators should underpin assessments of ecosystem recovery, yet monitoring many indicators is costly and their integration is challenging. Our objective was to combine indicators through a Bayesian hierarchical model to provide a comprehensive assessment of ecosystem status and identify a cost‐effective subset of indicators to provide an accurate estimate of ecosystem recovery. We assessed 59 aboveground–belowground indicators, classified into nine components of composition, structure, and function, to estimate the ecosystem status of restored rock spoils and reference forests in south‐eastern Australia. Overall ecosystem status, which integrates across ecosystem components and supporting indicators, was lower within restored forests but positively correlated with forest age. Reference forests had greater aboveground and belowground biotic structure, organic matter supply, and soil carbon stability, and trends were consistent among all of their supporting indicators. A subset of organic matter quality and nutrient cycling indicators were greater within restored forests, suggesting high ecosystem process rates, but that soil carbon may be more vulnerable to loss. Aboveground biotic structure was correlated with organic matter supply and quality, stability of soil carbon, the cycling of nutrients, and belowground biotic structure, providing evidence of aboveground–belowground coupling. A combination of four indicators representing belowground biotic structure, soil carbon stability, organic matter supply, and aboveground composition, provided a good estimate of ecosystem status at a third of the cost. Although ecosystem status can be monitored with a small set of indicators, a diversity of aboveground–belowground indicators provide a robust and comprehensive assessment of recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".